Efficient and Scalable Subgraph Search from Big Graphs in Cloud

这个项目寻求开发的知识，将形成下一代结构知识的图形搜索引擎的基础。子图搜索在许多处理大图的应用中都有很高的需求，如社交网络营销、犯罪检测和销售模式发现。然而，当搜索需要在云环境中进行时，存在许多挑战。该项目旨在开发高效、可扩展的查询处理算法，从存储在云中的机器集群中的大图中搜索子图。预期的结果可能会为云中的子图搜索奠定理论基础，并建立一个整合了各种查询语义的统一的图搜索引擎，供多种应用使用。

This project seeks to develop knowledge that will form the foundation for next-generation graph search engines of structural knowledge. Subgraph search is in high demand for many applications that deal with big graphs, such as social network marketing, crime detection and sales pattern discovery. However, there are many challenges when the search needs to be conducted in cloud environments. This project aims to develop efficient and scalable query processing algorithms to search subgraphs from a big graph stored in a cluster of machines in the cloud. The expected outcomes may lay the theoretical foundations for subgraph search in the cloud, and establish a unified graph search engine that integrates various query semantics for use in many applications.